Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations1000
Missing cells549
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric6
Categorical5

Alerts

Trip_Distance_km is highly overall correlated with Trip_PriceHigh correlation
Trip_Price is highly overall correlated with Trip_Distance_kmHigh correlation
Trip_Distance_km has 50 (5.0%) missing values Missing
Time_of_Day has 50 (5.0%) missing values Missing
Day_of_Week has 50 (5.0%) missing values Missing
Passenger_Count has 50 (5.0%) missing values Missing
Traffic_Conditions has 50 (5.0%) missing values Missing
Weather has 50 (5.0%) missing values Missing
Base_Fare has 50 (5.0%) missing values Missing
Per_Km_Rate has 50 (5.0%) missing values Missing
Per_Minute_Rate has 50 (5.0%) missing values Missing
Trip_Duration_Minutes has 50 (5.0%) missing values Missing
Trip_Price has 49 (4.9%) missing values Missing

Reproduction

Analysis started2025-02-13 22:02:12.027122
Analysis finished2025-02-13 22:02:14.316445
Duration2.29 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Trip_Distance_km
Real number (ℝ)

High correlation  Missing 

Distinct867
Distinct (%)91.3%
Missing50
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean27.070547
Minimum1.23
Maximum146.06705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-13T19:02:14.364842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.23
5-th percentile3.2245
Q112.6325
median25.83
Q338.405
95-th percentile48.3065
Maximum146.06705
Range144.83705
Interquartile range (IQR)25.7725

Descriptive statistics

Standard deviation19.9053
Coefficient of variation (CV)0.73531207
Kurtosis9.6532589
Mean27.070547
Median Absolute Deviation (MAD)12.965
Skewness2.23601
Sum25717.02
Variance396.22096
MonotonicityNot monotonic
2025-02-13T19:02:14.428117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 3
 
0.3%
9.91 3
 
0.3%
32.04 3
 
0.3%
10.14 3
 
0.3%
25.63 3
 
0.3%
3.22 3
 
0.3%
31.29 3
 
0.3%
13.64 3
 
0.3%
29.95 2
 
0.2%
46.9 2
 
0.2%
Other values (857) 922
92.2%
(Missing) 50
 
5.0%
ValueCountFrequency (%)
1.23 1
0.1%
1.25 1
0.1%
1.27 1
0.1%
1.34 1
0.1%
1.45 1
0.1%
1.53 1
0.1%
1.54 1
0.1%
1.56 1
0.1%
1.64 2
0.2%
1.71 2
0.2%
ValueCountFrequency (%)
146.0670472 1
0.1%
145.7470599 1
0.1%
139.4765146 1
0.1%
139.0622302 1
0.1%
138.7638872 1
0.1%
138.0983279 1
0.1%
130.8090013 1
0.1%
126.5476283 1
0.1%
122.820191 1
0.1%
116.6676807 1
0.1%

Time_of_Day
Categorical

Missing 

Distinct4
Distinct (%)0.4%
Missing50
Missing (%)5.0%
Memory size7.9 KiB
Afternoon
371 
Morning
283 
Evening
203 
Night
93 

Length

Max length9
Median length7
Mean length7.5852632
Min length5

Characters and Unicode

Total characters7206
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning
2nd rowAfternoon
3rd rowEvening
4th rowEvening
5th rowEvening

Common Values

ValueCountFrequency (%)
Afternoon 371
37.1%
Morning 283
28.3%
Evening 203
20.3%
Night 93
 
9.3%
(Missing) 50
 
5.0%

Length

2025-02-13T19:02:14.480182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T19:02:14.518073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
afternoon 371
39.1%
morning 283
29.8%
evening 203
21.4%
night 93
 
9.8%

Most occurring characters

ValueCountFrequency (%)
n 1714
23.8%
o 1025
14.2%
r 654
 
9.1%
i 579
 
8.0%
g 579
 
8.0%
e 574
 
8.0%
t 464
 
6.4%
A 371
 
5.1%
f 371
 
5.1%
M 283
 
3.9%
Other values (4) 592
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1714
23.8%
o 1025
14.2%
r 654
 
9.1%
i 579
 
8.0%
g 579
 
8.0%
e 574
 
8.0%
t 464
 
6.4%
A 371
 
5.1%
f 371
 
5.1%
M 283
 
3.9%
Other values (4) 592
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1714
23.8%
o 1025
14.2%
r 654
 
9.1%
i 579
 
8.0%
g 579
 
8.0%
e 574
 
8.0%
t 464
 
6.4%
A 371
 
5.1%
f 371
 
5.1%
M 283
 
3.9%
Other values (4) 592
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1714
23.8%
o 1025
14.2%
r 654
 
9.1%
i 579
 
8.0%
g 579
 
8.0%
e 574
 
8.0%
t 464
 
6.4%
A 371
 
5.1%
f 371
 
5.1%
M 283
 
3.9%
Other values (4) 592
 
8.2%

Day_of_Week
Categorical

Missing 

Distinct2
Distinct (%)0.2%
Missing50
Missing (%)5.0%
Memory size7.9 KiB
Weekday
655 
Weekend
295 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6650
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeekday
2nd rowWeekday
3rd rowWeekend
4th rowWeekday
5th rowWeekday

Common Values

ValueCountFrequency (%)
Weekday 655
65.5%
Weekend 295
29.5%
(Missing) 50
 
5.0%

Length

2025-02-13T19:02:14.555956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T19:02:14.579922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
weekday 655
68.9%
weekend 295
31.1%

Most occurring characters

ValueCountFrequency (%)
e 2195
33.0%
W 950
14.3%
k 950
14.3%
d 950
14.3%
a 655
 
9.8%
y 655
 
9.8%
n 295
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2195
33.0%
W 950
14.3%
k 950
14.3%
d 950
14.3%
a 655
 
9.8%
y 655
 
9.8%
n 295
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2195
33.0%
W 950
14.3%
k 950
14.3%
d 950
14.3%
a 655
 
9.8%
y 655
 
9.8%
n 295
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2195
33.0%
W 950
14.3%
k 950
14.3%
d 950
14.3%
a 655
 
9.8%
y 655
 
9.8%
n 295
 
4.4%

Passenger_Count
Categorical

Missing 

Distinct4
Distinct (%)0.4%
Missing50
Missing (%)5.0%
Memory size7.9 KiB
3.0
251 
2.0
241 
1.0
238 
4.0
220 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2850
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 251
25.1%
2.0 241
24.1%
1.0 238
23.8%
4.0 220
22.0%
(Missing) 50
 
5.0%

Length

2025-02-13T19:02:14.613122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T19:02:14.641136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 251
26.4%
2.0 241
25.4%
1.0 238
25.1%
4.0 220
23.2%

Most occurring characters

ValueCountFrequency (%)
. 950
33.3%
0 950
33.3%
3 251
 
8.8%
2 241
 
8.5%
1 238
 
8.4%
4 220
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 950
33.3%
0 950
33.3%
3 251
 
8.8%
2 241
 
8.5%
1 238
 
8.4%
4 220
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 950
33.3%
0 950
33.3%
3 251
 
8.8%
2 241
 
8.5%
1 238
 
8.4%
4 220
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 950
33.3%
0 950
33.3%
3 251
 
8.8%
2 241
 
8.5%
1 238
 
8.4%
4 220
 
7.7%

Traffic_Conditions
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing50
Missing (%)5.0%
Memory size7.9 KiB
Low
397 
Medium
371 
High
182 

Length

Max length6
Median length4
Mean length4.3631579
Min length3

Characters and Unicode

Total characters4145
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowHigh
3rd rowHigh
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
Low 397
39.7%
Medium 371
37.1%
High 182
18.2%
(Missing) 50
 
5.0%

Length

2025-02-13T19:02:14.682536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T19:02:14.709944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 397
41.8%
medium 371
39.1%
high 182
19.2%

Most occurring characters

ValueCountFrequency (%)
i 553
13.3%
L 397
9.6%
o 397
9.6%
w 397
9.6%
M 371
9.0%
e 371
9.0%
d 371
9.0%
u 371
9.0%
m 371
9.0%
H 182
 
4.4%
Other values (2) 364
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 553
13.3%
L 397
9.6%
o 397
9.6%
w 397
9.6%
M 371
9.0%
e 371
9.0%
d 371
9.0%
u 371
9.0%
m 371
9.0%
H 182
 
4.4%
Other values (2) 364
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 553
13.3%
L 397
9.6%
o 397
9.6%
w 397
9.6%
M 371
9.0%
e 371
9.0%
d 371
9.0%
u 371
9.0%
m 371
9.0%
H 182
 
4.4%
Other values (2) 364
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 553
13.3%
L 397
9.6%
o 397
9.6%
w 397
9.6%
M 371
9.0%
e 371
9.0%
d 371
9.0%
u 371
9.0%
m 371
9.0%
H 182
 
4.4%
Other values (2) 364
8.8%

Weather
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing50
Missing (%)5.0%
Memory size7.9 KiB
Clear
667 
Rain
227 
Snow
 
56

Length

Max length5
Median length5
Mean length4.7021053
Min length4

Characters and Unicode

Total characters4467
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowClear
5th rowClear

Common Values

ValueCountFrequency (%)
Clear 667
66.7%
Rain 227
 
22.7%
Snow 56
 
5.6%
(Missing) 50
 
5.0%

Length

2025-02-13T19:02:14.916695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-13T19:02:14.945570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
clear 667
70.2%
rain 227
 
23.9%
snow 56
 
5.9%

Most occurring characters

ValueCountFrequency (%)
a 894
20.0%
C 667
14.9%
l 667
14.9%
e 667
14.9%
r 667
14.9%
n 283
 
6.3%
R 227
 
5.1%
i 227
 
5.1%
S 56
 
1.3%
o 56
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 894
20.0%
C 667
14.9%
l 667
14.9%
e 667
14.9%
r 667
14.9%
n 283
 
6.3%
R 227
 
5.1%
i 227
 
5.1%
S 56
 
1.3%
o 56
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 894
20.0%
C 667
14.9%
l 667
14.9%
e 667
14.9%
r 667
14.9%
n 283
 
6.3%
R 227
 
5.1%
i 227
 
5.1%
S 56
 
1.3%
o 56
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 894
20.0%
C 667
14.9%
l 667
14.9%
e 667
14.9%
r 667
14.9%
n 283
 
6.3%
R 227
 
5.1%
i 227
 
5.1%
S 56
 
1.3%
o 56
 
1.3%

Base_Fare
Real number (ℝ)

Missing 

Distinct290
Distinct (%)30.5%
Missing50
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean3.5029895
Minimum2.01
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-13T19:02:14.996435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.01
5-th percentile2.1645
Q12.73
median3.52
Q34.26
95-th percentile4.85
Maximum5
Range2.99
Interquartile range (IQR)1.53

Descriptive statistics

Standard deviation0.87016193
Coefficient of variation (CV)0.24840552
Kurtosis-1.2132238
Mean3.5029895
Median Absolute Deviation (MAD)0.76
Skewness-0.0051492734
Sum3327.84
Variance0.75718178
MonotonicityNot monotonic
2025-02-13T19:02:15.058686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.94 10
 
1.0%
3.88 8
 
0.8%
2.8 8
 
0.8%
2.32 8
 
0.8%
3.89 7
 
0.7%
4.6 7
 
0.7%
2.17 7
 
0.7%
3.13 7
 
0.7%
2.38 7
 
0.7%
2.43 7
 
0.7%
Other values (280) 874
87.4%
(Missing) 50
 
5.0%
ValueCountFrequency (%)
2.01 1
 
0.1%
2.02 3
0.3%
2.03 2
 
0.2%
2.04 2
 
0.2%
2.05 5
0.5%
2.06 4
0.4%
2.07 2
 
0.2%
2.08 4
0.4%
2.09 1
 
0.1%
2.1 3
0.3%
ValueCountFrequency (%)
5 2
 
0.2%
4.99 4
0.4%
4.97 4
0.4%
4.96 5
0.5%
4.95 3
0.3%
4.94 5
0.5%
4.93 3
0.3%
4.92 3
0.3%
4.91 3
0.3%
4.9 7
0.7%

Per_Km_Rate
Real number (ℝ)

Missing 

Distinct150
Distinct (%)15.8%
Missing50
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1.2333158
Minimum0.5
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-13T19:02:15.111700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5845
Q10.86
median1.22
Q31.61
95-th percentile1.91
Maximum2
Range1.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.42981587
Coefficient of variation (CV)0.34850431
Kurtosis-1.1986632
Mean1.2333158
Median Absolute Deviation (MAD)0.37
Skewness0.079206429
Sum1171.65
Variance0.18474168
MonotonicityNot monotonic
2025-02-13T19:02:15.166603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.63 15
 
1.5%
1.5 13
 
1.3%
1.9 12
 
1.2%
1.92 12
 
1.2%
0.79 12
 
1.2%
1.71 12
 
1.2%
0.7 11
 
1.1%
1.3 11
 
1.1%
1.86 11
 
1.1%
0.82 11
 
1.1%
Other values (140) 830
83.0%
(Missing) 50
 
5.0%
ValueCountFrequency (%)
0.5 2
 
0.2%
0.51 9
0.9%
0.52 2
 
0.2%
0.53 5
0.5%
0.54 3
 
0.3%
0.55 4
0.4%
0.56 9
0.9%
0.57 8
0.8%
0.58 6
0.6%
0.59 4
0.4%
ValueCountFrequency (%)
2 2
 
0.2%
1.99 2
 
0.2%
1.98 5
0.5%
1.97 3
 
0.3%
1.96 4
 
0.4%
1.95 5
0.5%
1.94 8
0.8%
1.93 3
 
0.3%
1.92 12
1.2%
1.91 8
0.8%

Per_Minute_Rate
Real number (ℝ)

Missing 

Distinct41
Distinct (%)4.3%
Missing50
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.29291579
Minimum0.1
Maximum0.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-13T19:02:15.216209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.12
Q10.19
median0.29
Q30.39
95-th percentile0.48
Maximum0.5
Range0.4
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.11559177
Coefficient of variation (CV)0.39462458
Kurtosis-1.1992318
Mean0.29291579
Median Absolute Deviation (MAD)0.1
Skewness0.058695065
Sum278.27
Variance0.013361458
MonotonicityNot monotonic
2025-02-13T19:02:15.270845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.15 35
 
3.5%
0.28 33
 
3.3%
0.18 32
 
3.2%
0.38 31
 
3.1%
0.23 30
 
3.0%
0.12 30
 
3.0%
0.42 27
 
2.7%
0.31 27
 
2.7%
0.2 27
 
2.7%
0.11 26
 
2.6%
Other values (31) 652
65.2%
(Missing) 50
 
5.0%
ValueCountFrequency (%)
0.1 14
 
1.4%
0.11 26
2.6%
0.12 30
3.0%
0.13 21
2.1%
0.14 22
2.2%
0.15 35
3.5%
0.16 21
2.1%
0.17 24
2.4%
0.18 32
3.2%
0.19 25
2.5%
ValueCountFrequency (%)
0.5 9
 
0.9%
0.49 24
2.4%
0.48 21
2.1%
0.47 20
2.0%
0.46 21
2.1%
0.45 21
2.1%
0.44 21
2.1%
0.43 21
2.1%
0.42 27
2.7%
0.41 19
1.9%

Trip_Duration_Minutes
Real number (ℝ)

Missing 

Distinct917
Distinct (%)96.5%
Missing50
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean62.118116
Minimum5.01
Maximum119.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-13T19:02:15.327171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.01
5-th percentile11.4025
Q135.8825
median61.86
Q389.055
95-th percentile112.7815
Maximum119.84
Range114.83
Interquartile range (IQR)53.1725

Descriptive statistics

Standard deviation32.154406
Coefficient of variation (CV)0.51763332
Kurtosis-1.1228564
Mean62.118116
Median Absolute Deviation (MAD)26.44
Skewness0.017749129
Sum59012.21
Variance1033.9059
MonotonicityNot monotonic
2025-02-13T19:02:15.389702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.21 3
 
0.3%
113.49 2
 
0.2%
94.11 2
 
0.2%
30.31 2
 
0.2%
65.42 2
 
0.2%
84 2
 
0.2%
107.25 2
 
0.2%
109.53 2
 
0.2%
44.99 2
 
0.2%
59.81 2
 
0.2%
Other values (907) 929
92.9%
(Missing) 50
 
5.0%
ValueCountFrequency (%)
5.01 1
0.1%
5.05 1
0.1%
5.1 1
0.1%
5.5 1
0.1%
5.61 1
0.1%
5.65 1
0.1%
5.66 1
0.1%
5.93 1
0.1%
6.06 2
0.2%
6.1 1
0.1%
ValueCountFrequency (%)
119.84 1
0.1%
119.82 1
0.1%
119.65 1
0.1%
119.54 1
0.1%
119.21 1
0.1%
119.17 1
0.1%
119.08 1
0.1%
118.44 1
0.1%
118.39 1
0.1%
117.94 1
0.1%

Trip_Price
Real number (ℝ)

High correlation  Missing 

Distinct951
Distinct (%)100.0%
Missing49
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean56.874773
Minimum6.1269
Maximum332.04369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-13T19:02:15.443220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.1269
5-th percentile18.8131
Q133.74265
median50.0745
Q369.09935
95-th percentile102.3809
Maximum332.04369
Range325.91679
Interquartile range (IQR)35.3567

Descriptive statistics

Standard deviation40.469791
Coefficient of variation (CV)0.71155959
Kurtosis19.892281
Mean56.874773
Median Absolute Deviation (MAD)17.167
Skewness3.7325606
Sum54087.909
Variance1637.8039
MonotonicityNot monotonic
2025-02-13T19:02:15.496919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.3545 1
 
0.1%
65.195 1
 
0.1%
85.4652 1
 
0.1%
69.9148 1
 
0.1%
72.1607 1
 
0.1%
36.4212 1
 
0.1%
41.0143 1
 
0.1%
33.2317 1
 
0.1%
38.9635 1
 
0.1%
24.6913 1
 
0.1%
Other values (941) 941
94.1%
(Missing) 49
 
4.9%
ValueCountFrequency (%)
6.1269 1
0.1%
6.4729 1
0.1%
8.6709 1
0.1%
8.7296 1
0.1%
8.9203 1
0.1%
9.8005 1
0.1%
9.8718 1
0.1%
9.9494 1
0.1%
10.2366 1
0.1%
10.4444 1
0.1%
ValueCountFrequency (%)
332.0436887 1
0.1%
329.9130039 1
0.1%
328.8717691 1
0.1%
327.2176655 1
0.1%
325.0989498 1
0.1%
322.7259961 1
0.1%
320.9586636 1
0.1%
296.0886973 1
0.1%
283.6452006 1
0.1%
280.8773016 1
0.1%

Interactions

2025-02-13T19:02:13.778167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.271124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.585682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.876372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.187167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.495270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.827977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.327329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.637730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.925152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.238093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.542379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.873259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.377333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.685401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.977044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.287021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.589622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.918439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.428038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.734172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.027962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.342471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.636952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.967778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.481121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.783956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.085604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.394819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.686564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:14.018057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.530088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:12.830061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.138149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.445465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-13T19:02:13.733542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-13T19:02:15.541438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Base_FareDay_of_WeekPassenger_CountPer_Km_RatePer_Minute_RateTime_of_DayTraffic_ConditionsTrip_Distance_kmTrip_Duration_MinutesTrip_PriceWeather
Base_Fare1.0000.0000.0000.001-0.0180.0000.0440.0450.0130.0460.077
Day_of_Week0.0001.0000.0000.0000.0490.0450.0000.0000.0000.0000.000
Passenger_Count0.0000.0001.0000.0000.0000.0000.0300.0280.0000.0000.040
Per_Km_Rate0.0010.0000.0001.0000.0320.0200.000-0.0350.0270.3890.011
Per_Minute_Rate-0.0180.0490.0000.0321.0000.0420.079-0.021-0.0260.2650.000
Time_of_Day0.0000.0450.0000.0200.0421.0000.0000.0000.0000.0000.000
Traffic_Conditions0.0440.0000.0300.0000.0790.0001.0000.0440.0000.0860.000
Trip_Distance_km0.0450.0000.028-0.035-0.0210.0000.0441.000-0.0360.7220.006
Trip_Duration_Minutes0.0130.0000.0000.027-0.0260.0000.000-0.0361.0000.3480.000
Trip_Price0.0460.0000.0000.3890.2650.0000.0860.7220.3481.0000.000
Weather0.0770.0000.0400.0110.0000.0000.0000.0060.0000.0001.000

Missing values

2025-02-13T19:02:14.122409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-13T19:02:14.175967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-13T19:02:14.259308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Trip_Distance_kmTime_of_DayDay_of_WeekPassenger_CountTraffic_ConditionsWeatherBase_FarePer_Km_RatePer_Minute_RateTrip_Duration_MinutesTrip_Price
019.35MorningWeekday3.0LowClear3.560.800.3253.8236.2624
147.59AfternoonWeekday1.0HighClearNaN0.620.4340.57NaN
236.87EveningWeekend1.0HighClear2.701.210.1537.2752.9032
330.33EveningWeekday4.0LowNaN3.480.510.15116.8136.4698
4NaNEveningWeekday3.0HighClear2.930.630.3222.6415.6180
58.64AfternoonWeekend2.0MediumClear2.551.710.4889.3360.2028
63.85AfternoonWeekday4.0HighRain3.511.66NaN5.0511.2645
743.44EveningWeekend3.0NaNClear2.971.870.23NaN101.1216
830.45MorningWeekday3.0HighClear2.771.780.34110.33NaN
935.70AfternoonWeekday2.0LowRain3.391.520.47NaN75.5657
Trip_Distance_kmTime_of_DayDay_of_WeekPassenger_CountTraffic_ConditionsWeatherBase_FarePer_Km_RatePer_Minute_RateTrip_Duration_MinutesTrip_Price
99040.17EveningWeekday3.0LowClear3.810.660.4262.6656.6394
99135.04MorningWeekend4.0MediumRain2.901.100.159.99NaN
99214.34AfternoonWeekday1.0MediumClear3.231.010.2945.0730.7837
993NaNMorningWeekday3.0MediumClear2.651.35NaN25.6155.3348
99418.69EveningWeekday3.0MediumClear4.901.790.1779.4151.8548
9955.49AfternoonWeekend4.0MediumClear2.390.620.4958.3934.4049
99645.95NightWeekday4.0MediumClear3.120.61NaN61.9662.1295
9977.70MorningWeekday3.0LowRain2.081.78NaN54.1833.1236
99847.56MorningWeekday1.0LowClear2.670.820.17114.9461.2090
99922.85MorningWeekend3.0MediumClear4.34NaN0.2329.6945.4437